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Constant Force-Tracking Control Based on Deep Reinforcement Learning in Dynamic Auscultation Environment
Tieyi Zhang1,2, Chao Chen1,2, Minglei Shu1,2
1School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China.
Sensors (Basel, Switzerland)
|February 28, 2023
Summary
This study introduces a deep reinforcement learning approach for intelligent medical robots performing dynamic auscultation. The novel control strategy ensures safe and accurate constant force tracking during simulated and real-world medical examinations.
Area of Science:
- Robotics
- Artificial Intelligence
- Medical Technology
Background:
- Intelligent medical robots can assist healthcare professionals with diagnoses and treatments, addressing personnel shortages.
- Dynamic medical auscultation presents challenges for robotic control due to the breathing process.
Purpose of the Study:
- To investigate the application of deep reinforcement learning for dynamic medical auscultation tasks.
- To develop a robust control strategy for intelligent medical robots in auscultation.
Main Methods:
- A constant force-tracking control method for dynamic environments was proposed.
- A physically characteristic modeling method was used to simulate dynamic breathing.
- An optimal reward function was designed for efficient control strategy learning.
Main Results:
- Simulation experiments showed tracking error within ±0.5 N for normal force.
- Real-world tests demonstrated effective constant force-tracking in medical auscultation.
- Contact force remained within a safe and stable range, averaging approximately 5.2 N.
Conclusions:
- The developed deep reinforcement learning control strategy is effective for dynamic medical auscultation.
- The approach ensures safe and stable robotic interaction during auscultation procedures.
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